The Future of Glass Optics Replication

Kreilkamp, Holger; Staasmeyer, Jan-Helge; Niendorf, Laura

doi:10.1002/opph.201600023

Cited by 2 publications

(3 citation statements)

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“…In the existing production, accuracy and possible glass defects can only be realized at the end of the forming process by relevant off-line measurements when the molded components are entirely cooled down at room temperature [14][15][16]. It is, however, emphasized that imperfections of the molded components such as form deviation, chill ripples, or glass cracks are primarily driven by the molding step [17][18][19][20].…”

Section: Fig 1 Process Chain Of Thin Glass Formingmentioning

confidence: 99%

“…Those are the common defects observed in the nonisothermal molding process, mainly resulted from the high heat exchanges at the glass-mold interface [21,22] as well as the complex thermo-viscoelastic material behaviors of glass at elevated molding temperatures [23][24][25]. In serial production, if the defects are not detected during the molding step, it commonly results in a large number of glass rejects and high energy cost vain to anneal the glass failures [26]. Accordingly, the glass optic manufacturing industry is steadily raising demands on real-time quality control in the hot forming of thin glasses.…”

Section: Fig 1 Process Chain Of Thin Glass Formingmentioning

confidence: 99%

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Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Vogel

Subramanian

et al. 2022

KEM

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Towards the growing trends in lightweight, flexible, and optical advantages, thin glasses become key components in numerous applications such as consumer electronics like foldable smartphones, or automotive interiors. Nonisothermal glass molding promises a viable technology for the cost-efficient production of precision glass components. In the existing production, the quality of the glass products can only be accessed at the end of the hot forming process. Due to high rates of product failures often appeared in the precision glass molding processes, the current quality control of the produced optical products suffers low process efficiency. This work introduces an enabling approach for monitoring the product quality in real-time using thermography and machine learning. Specifically, the acquisition of the temperature fields of the glass components during the hot forming stage enabled by an infrared thermographic camera allows machine learning to predict the final shape of the molded components at the end of the forming process. Several transfer learning models have been investigated to demonstrate the proposed method. To further enhance the prediction performance, self-built convolutional neural network models were developed using different types of image data. By incorporating the time-series image data as an input to the learning models, the prediction performance was achieved. The model built in the present work demonstrates an excellent prediction accuracy where the difference between the measured and predicted shapes of the glass products can be kept at low double-digit micrometers. Such accuracy achieved by our self-developed machine learning model promisingly satisfy the quality control in serial productions of numerous precision optical glass components in automotive and consumer electronics sectors.

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Section: Fig 1 Process Chain Of Thin Glass Formingmentioning

confidence: 99%

Section: Fig 1 Process Chain Of Thin Glass Formingmentioning

confidence: 99%

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Vogel

Subramanian

et al. 2022

KEM

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“…However, it is a necessary step to realize the Industry 4.0 of PGM. An effort has been placed to develop independent, web-based software for PGM, which can run on standard devices like laptops, smartphones and PCs [95] for all users to share, including process developers and quality control inspectors. In this way, one can collect all the digitalized data throughout the manufacturing chain of PGM, and further analyze the correlations between "input" preform, mold, processing parameters and "output" quality of the molded glass lenses.…”

Section: Residual Stress Optimizationmentioning

confidence: 99%

Precision glass molding: Toward an optimal fabrication of optical lenses

Zhang

Liu

2017

Front. Mech. Eng.

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It is costly and time consuming to use machining processes, such as grinding, polishing and lapping, to produce optical glass lenses with complex features. Precision glass molding (PGM) has thus been developed to realize an efficient manufacture of such optical components in a single step. However, PGM faces various technical challenges. For example, a PGM process must be carried out within the super-cooled region of optical glass above its glass transition temperature, in which the material has an unstable non-equilibrium structure. Within a narrow window of allowable temperature variation, the glass viscosity can change from 10 5 to 10 12 Pa$s due to the kinetic fragility of the super-cooled liquid. This makes a PGM process sensitive to its molding temperature. In addition, because of the structural relaxation in this temperature window, the atomic structure that governs the material properties is strongly dependent on time and thermal history. Such complexity often leads to residual stresses and shape distortion in a lens molded, causing unexpected changes in density and refractive index. This review will discuss some of the central issues in PGM processes and provide a method based on a manufacturing chain consideration from mold material selection, property and deformation characterization of optical glass to process optimization. The realization of such optimization is a necessary step for the Industry 4.0 of PGM.

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The Future of Glass Optics Replication

Cited by 2 publications

References 0 publications

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Real-Time Quality Control in Thin Glass Forming Using Infrared Thermography and Deep Learning

Precision glass molding: Toward an optimal fabrication of optical lenses

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